There are 400 extracted 128-\(D\)  dimensional global image descriptors from the ICDAR13 train set after performing normaliztion and dimensio`nality reduction. We further reduced their dimensionality using t-SNE [1]} projection for visualisation purposes. The t-SNE plot of the embeddings space is shown in Figure REF . The plot shows that the embeddings learned by the proposed pipeline has very good discriminative properties as every 4 global images descriptors of each writer approximately lie near each other. In other words, nearest neighbours of each global images descriptor stem from the same writer. On the other hand, Figure REF  depicts 1000 global image descriptors of dimension 128 of ICDAR13 test set before and after re-ranking part. As the Figure illustrates, discriminativity of clusters are increased and the clusters belonging to each writer are more distinguishable from each other.
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